AI-Assisted Optimization of Supersonic Airfoil Shapes Using CFD Coupling

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 16 | 01 | Page :
    By

    Amit Pandhare,

  • Aditya Kumbhar,

  • Vaibhav Godase,

  1. UG Students, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
  2. UG Student, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
  3. Assistant Professor, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India

Abstract

This paper presents a novel framework for optimizing supersonic airfoil geometries through integrated artificial intelligence and computational fluid dynamics coupling. Traditional gradient-based optimization methods for high-speed aerodynamic shapes suffer from computational expense and convergence difficulties in non-convex design spaces. The proposed methodology employs a deep neural network surrogate model trained on high-fidelity Reynolds-Averaged Navier-Stokes solutions to approximate aerodynamic performance metrics across the design space. A hybrid particle swarm-genetic algorithm searches this surrogate landscape to identify optimal geometries, with periodic CFD validation ensuring solution fidelity. A baseline NACA 64A-series airfoil operating at Mach 2.0 and a Reynolds number of 1.0×10¹ under stable compressible flow conditions is used to test the framework. The optimization findings show that the structural thickness, aerodynamic stability, and manufacturability constraints are maintained while the drag coefficient is reduced by 23.4% and the lift-to-drag ratio is improved by 31.2%. Through improved leading-edge curvature and camber redistribution, the modified geometry shows better shock location, less wave drag, smoother pressure gradients, and increased aerodynamic efficiency. The efficiency, scalability, and appropriateness of the suggested framework for next- generation high-speed aerodynamic design applications are confirmed by the 87% reduction in overall computational cost when compared to traditional direct CFD-driven optimization. Applied to baseline NACA 64A-series airfoil at Mach 2.0 and Reynolds number 1.0×10⁷, the optimization achieves 23.4% drag reduction and 31.2% lift-to-drag ratio improvement while maintaining structural thickness constraints. The optimized geometry exhibits favorable shock positioning and reduced wave drag through refined leading-edge geometry and camber distribution. Computational cost is reduced by 87% compared to direct CFD-based optimization.

Keywords: Supersonic aerodynamics, Airfoil optimization, Computational fluid dynamics, Neural network surrogate, Particle swarm optimization, Drag reduction, High-speed flow.

How to cite this article:
Amit Pandhare, Aditya Kumbhar, Vaibhav Godase. AI-Assisted Optimization of Supersonic Airfoil Shapes Using CFD Coupling. Journal of Aerospace Engineering & Technology. 2026; 16(01):-.
How to cite this URL:
Amit Pandhare, Aditya Kumbhar, Vaibhav Godase. AI-Assisted Optimization of Supersonic Airfoil Shapes Using CFD Coupling. Journal of Aerospace Engineering & Technology. 2026; 16(01):-. Available from: https://journals.stmjournals.com/joaet/article=2026/view=238995


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Ahead of Print Subscription Review Article
Volume 16
01
Received 16/02/2026
Accepted 19/02/2026
Published 21/03/2026
Publication Time 33 Days
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